Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa.

ABSTRACT:

Aim

HIV prevention measures in sub-Saharan Africa are still short of attaining the UNAIDS 90-90-90 fast track targets set in 2014. Identifying predictors for HIV status may facilitate targeted screening interventions that improve health care. We aimed at identifying HIV predictors as well as predicting persons at high risk of the infection.

Method

We applied machine learning approaches for building models using population-based HIV Impact Assessment (PHIA) data for 41,939 male and 45,105 female respondents with 30 and 40 variables respectively from four countries in sub-Saharan countries. We trained and validated the algorithms on 80% of the data and tested on the remaining 20% where we rotated around the left-out country. An algorithm with the best mean f1 score was retaine

SUBMITTER: Mutai CK 

PROVIDER: S-EPMC8325403 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature